AI & Data
Engineering for dependable data flows and AI capabilities built into products and business processes.
AI is only as dependable as the data it receives and the system around it. We build both: the pipelines and storage that feed it, and the integration, orchestration and evaluation that make it safe to rely on.
What this covers
Each area is scoped around the application and the decisions it supports.
Connect data sources, define transformations, and coordinate processing workflows. Address validation, failures, access, and traceability so that downstream systems can rely on the data they receive.
Design how applications store, retrieve, and share data. Select structures and access patterns around transaction needs, analytical use, performance, retention, and operating constraints.
Integrate AI capabilities into a defined application or workflow. Establish system interfaces, instructions, permitted actions, data boundaries, and human review appropriate to the intended use.
Coordinate AI steps with conventional services, data processing, and human decisions. Define handoffs, failure handling, and limits on automated actions rather than treating each model interaction as an isolated feature.
Define how behaviour is assessed against requirements, including failure cases and the need for human intervention. Address output quality, latency, cost, monitoring, and change control for the agreed application.
Work that used this capability
Case studies, described as they were built.
One gateway to 45 models from six AI providers, with guardrails and tracing
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Applying LLMs row by row to business data, with structured and predictable output
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A pipeline engine that runs, pauses and resumes data workflows
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Connecting AI workflows to Outlook and SharePoint through Microsoft Graph
Read case studyServices that draw on it
Common questions
Do you work with large language models?
Yes. Our case studies include AI agents deployed as services, a gateway to 45 models from six providers, and pipelines that apply language models to business data with structured output.
How do you make AI output predictable?
By defining the output structure in advance, validating every result against it, and evaluating behaviour against requirements, including failure cases.
Discuss your initiative
Share the initiative, what it needs to achieve, and the constraints you already know. We can discuss the engineering work involved and whether CharCentric is the right partner.